Do General and Multiple Sclerosis-Specific Quality of Life Instruments Differ?
Bibliographic record
Abstract
BACKGROUND: Quality of life instruments provide information that traditional outcome measures used in studies of multiple sclerosis do not. It is unclear if longer, disease-specific instruments provide more useful information than shorter, more general instruments, or whether patients prefer one type to another. METHODS: We conducted a cross-sectional study of quality of life in a multiple sclerosis clinic population using a mailed questionnaire that combined three different quality of life instruments; the SF-36, the Multiple Sclerosis Quality of Life Instrument-54, and the EuroQol EQ-5D. We assessed the feasability of using each instrument and patient preference for each, calculated correlation coefficients for the summary scores of each instrument and other measures of disease severity, and calculated odds ratios from proportional odds models comparing each instrument with the Expanded Disability Status Scale. RESULTS: We did not find substantial differences between the three instruments. All were well-received by patients, and over 75% felt that the combination of the three instruments best assessed their quality of life. For each instrument there was substantial variability between patients with similar quality of life scores in terms of their disability (as assessed by the Expanded Disability Status Scale and their own perception of their disease severity and quality of life (on simple 1-10 scales). CONCLUSIONS: Quality of life instruments are easy to use and well-received by patients, regardless of their length. There do not appear to be clinically important differences between general and disease-specific instruments. Each instrument appears to measure something other than a patient's disability or perception of their own disease severity or quality of life.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.080 | 0.221 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".